A recently published paper by Hart et al presented a genome-wide CRISPR screening to identify fitness genes (a superset of essential genes) in five cell lines. The paper is quite impressive and shows the potentiality of CRISPR to generate large scale knockouts and to characterize the importance and function of genes in different conditions.

In the discussion the authors propose that fitness genes are more likely to be more conserved across species. However they do not follow-up on this hypothesis, probably for lack of space. They can’t be blamed as they already present a lot of results in the paper.

Distribution of conservation scores in the human genome. Are essential genes more conserved than other genes?

This post presents a follow-up analysis on the hypothesis that fitness genes are more conserved than non-essential genes. I’ll take the original data from the paper, get the conservation scores from bioconductor data packages, and do a Wilcoxon test to compare the two distribution. The full code is available as a github repository, and please feel free to contribute if you want to do some free R/Bioconductor analysis.

BioConductor includes many powerful packages for working with genomics data. You can do pretty much everything, from downloading gene coordinates and sequences of any model species, to converting gene ids and symbol, and to accessing ENCODE data and anything in UCSC, Ensembl, and other resources. However these packages are not always well known, and the initial learning curve is a steep, specially for R beginners.

This series of tutorials will describe how to get gene coordinates from bioconductor, intersect these with some interesting dataset from ENCODE, and do an enrichment analysis with DOSE. It will be fun 🙂

Libraries required for this tutorial

For this tutorial we will use only Human data. Most of the packages needed for working with human genomics can be installed with a single command:

org.Hs.eg.db is what you need to convert all gene ids – from entrez to ensembl, to GO, and so on.

The data in these packages is updated periodically (I think every 6 months), and is pretty stable, meaning that anybody using the same packages and version should be able to reproduce the same results. An alternative to using these data packages is biomaRt, but I prefer the data packages as they can be used without internet connection.

The package AnnotationHub is used to retrieve data from multiple sources and will be described later. The BSgenome package is for retrieving the human genome sequence: we will not use it in the tutorial but I included it for completeness.

Note that I also loaded the dplyr package for this tutorial. Although dplyr is not needed for working with genomics data, I consider it one of the most useful packages in R, and this tutorial will make heavy use of it. I apologize if this tutorial is not easy to follow to people not familiar with dplyr.

Retrieving gene and transcript coordinates

The TxDb object can be used to retrieve coordinates of genes, transcripts, and exons in the human genome. For example, we can access all human transcript with the transcript() function:

See help(transcripts) for other functions that can be applied to a TxDb object. For example, genes() retrieve coordinates of genes, while exons() and promoters() work similarly.

In the example above I also specified a “columns” parameter, in order to show the gene id as well. You can use this column to get the coordinates of a specific set of genes. For example, the following will retrieve the coordinates of the genes corresponding to entrez ids 1234, 231, and 421:

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>subset(human.transcripts,gene_id%in%c(1234,231,421))

GRanges objectwith5ranges and3metadata columns:

seqnamesranges strand|tx_id tx_name gene_id

<Rle><IRanges><Rle>|<integer><character><CharacterList>

[1]chr3[46411633,46417697]+|13703uc003cpo.41234

[2]chr3[46411633,46417697]+|13704uc010hjd.31234

[3]chr7[134127107,134143888]-|31418uc003vrp.1231

[4]chr22[19957402,19966734]-|74543uc002zqy.3421

[5]chr22[19957402,20004309]-|74544uc002zqz.3421

Converting Entrez IDs to symbols and other IDs

One of the most tricky part in bioinformatics is converting gene ids to symbols and other ids. Many errors can be made in this process, and is therefore very important to have a consistent way to convert gene ids.

Luckily, we can use the org.Hs.eg.db for easily converting many ids. This package should already have been loaded with library(Homo.sapiens). To see all the possible conversion tables (bimaps) available, we can either to library(help=org.Hs.eg.db) or simply write “org.Hs.eg” and then hit tab on the R command line .

One of my favorite bimaps is the one to convert gene symbols to entrez. As you may know, for historical reasons the same gene can have more than one symbol. This usually complicates things a lot, and a safe procedure is to always convert symbols to entrez before starting any analysis. The ALIAS2EG bimap is there for this type of conversion:

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>head(as.data.frame(org.Hs.egALIAS2EG))

gene_id alias_symbol

11A1B

21ABG

31GAB

41HYST2477

51A1BG

62A2MD

When you convert symbols to id, it is important to remember that not only the same gene can have more than one symbol, but also the same symbol can match multiple entrez ids. For example, here is the code to get which symbols match more than one entrez id in the human species:

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>as.data.frame(org.Hs.egALIAS2EG)%>%

count(alias_symbol)%>%

arrange(-n)

Source:local data frame[118,097x2]

alias_symbol n

(chr)(int)

1VH36

2MT112

3GPCR11

4HOX110

5ACT9

6HOX29

7PPIase9

8UDPGT9

9ALP8

10NAP18

Note: the “%>%” symbol and the count, arrange functions come from the dplyr package.

The only safe thing to do in these cases is to identify the duplicated symbols and either discard them or manually curate them. Let’s imagine that a fellow researchers asked us to retrieve information for the genes ACT, DOLPP1 and MGAT3. We can use the bimap to identify the genes that map to multiple entrez id, and then go back to our colleague and ask him to tell us which are the correct ids.

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>as.data.frame(org.Hs.egALIAS2EG)%>%

count(alias_symbol)%>%

filter(alias_symbol%in%mygenes)

Source:local data frame[3x2]

alias_symbol n

(chr)(int)

1ACT9

2DOLPP11

3MGAT32

In these examples I converted the bimap to a dataframe and then did an intersection. However the “bioConductor” way to use these bimaps is through the select function:

One big problem with these bioconductor packages is that they clash with many dplyr functions. For example, the select function gets overwritten if you load dplyr after Homo.sapiens, and the only option to avoid headaches is to explicitly refer to the function as AnnotationDbi::select. These conflicts in the namespace can cause a lot of confusion in R, because they let to weird error messages that are completely unrelated to the real problem.

In any case, the advantage of the select function is that it allows to retrieve more id types at the same type. For example here I retrieved both entrez id and ensembl ids, and if you type columns(org.Hs.eg.db) you will be able to see many other possible output columns.

Next parts of the tutorial

I was originally planning to write one big tutorial in the same post, but now I see that it would be much more readable if I split it into multiple posts.

Please let me know if you have any comment regarding this first tutorial, and I will try to improve it and take the feedback into account for the next parts.